“Blind” signal detection generally involves receiving and decoding incoming signals when the signal type is not known to the receiver. Many fields of science deal with this type of signal detection, and various techniques have been developed to identify an incoming signal of unknown type, so that its parameters, such as its modulation type and baud rate, can be known and used to decode the signal.
Several examples of signal recognition techniques are parameter-based algorithms, pattern recognition, algorithms that exploit cyclostationarity characteristics, and neural networks. U.S. Pat. No. 6,690,746, entitled “Signal Recognizer for Communications Signals”, assigned to Southwest Research Institute, discusses a system and method for classifying incoming signals as being one of a variety of signal types. Signal parameters are estimated and signals are demodulated in accordance with the estimated parameters.
A classic problem in signal detection is referred to as blind source separation, which involves detecting individual source signals from a signal containing a composite of signals. Two processing tools conventionally used for blind signal detection, based on higher-order (above second order) statistical analysis, are singular value decomposition (SVD) and independent components analysis (ICA), typically applied to spectrograms. SVD is a method of reducing the dimensionality of data while retaining maximum information content, and decomposes a spectrogram into a sum of vector outer products with vectors representing both the basis functions (eigenvectors) and the projected features (eigen coefficients). The basis functions and coefficients can be combined to form an eigenspectra, which are spectrograms that contain subsets of the original information. ICA can be used to perform a transformation of the SVD basis functions, yielding maximum separation of features.